AI tool comparison
LFM2.5-VL vs Ternary Bonsai
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
AI Models
LFM2.5-VL
450M vision-language model that runs in under 250ms on edge hardware
75%
Panel ship
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Community
Paid
Entry
Liquid AI just shipped LFM2.5-VL, a 450M-parameter vision-language model engineered from the ground up for edge deployment. Unlike most VLMs that require a beefy GPU in the cloud, LFM2.5-VL targets devices like the Snapdragon 8 Elite, NVIDIA Jetson Orin, and AMD Ryzen AI — hitting sub-250ms latency on-device without any cloud round-trip. This model builds significantly on its predecessor with four new capabilities: bounding box prediction (81.28 on RefCOCO-M), multilingual support across 8 languages, function calling, and improved instruction following. Those aren't just benchmark checkboxes — bounding box prediction means you can run visual grounding and object detection pipelines on a phone or robot without any server involvement. Liquid AI is the MIT-spun startup behind Liquid Foundation Models (LFMs), a non-Transformer architecture that delivers competitive performance at a fraction of the memory footprint. LFM2.5-VL is available free on HuggingFace and through Liquid's LEAP inference platform. For builders targeting on-device AI — robotics, mobile, embedded — this is one of the most practical releases of the month.
Open Source Models
Ternary Bonsai
1.58-bit LLMs that fit in 1.75 GB — runs in your browser via WebGPU
75%
Panel ship
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Community
Paid
Entry
PrismML's Ternary Bonsai is a family of ultra-compressed language models using 1.58-bit weights — meaning every parameter is stored as -1, 0, or +1, with no higher-precision layers anywhere in the architecture. The line-up covers 8B, 4B, and 1.7B parameter models. The flagship 8B model fits in 1.75 GB of RAM, a 9x reduction versus a 16-bit baseline. Unlike earlier 1-bit experiments that felt like a party trick with serious capability regressions, Ternary Bonsai 8B outperforms PrismML's own prior 1-bit Bonsai 8B by 5 points on average across standard benchmarks. The team also ships WebGPU inference, so the 1.7B model runs entirely in a browser tab. This is the first time a production-quality chat model has run with no server at all. The real-world use case is edge and offline deployment: medical devices, air-gapped government systems, consumer apps that need to work without a signal. At 1.75 GB, the 8B model fits on the GPU RAM of a six-year-old gaming laptop. PrismML is positioning this as the foundation for truly offline AI — a credible claim if the capability benchmarks hold up under real-world testing.
Reviewer scorecard
“Sub-250ms on-device vision with function calling is the unlock for a huge class of apps that couldn't tolerate cloud latency — real-time AR overlays, offline field inspection, privacy-sensitive medical imaging. The bounding box support is icing; ship this.”
“1.75 GB for an 8B model is a genuine engineering achievement. I can finally ship a capable model inside a desktop Electron app without requiring users to have a dedicated GPU. The WebGPU demo loads fast and output quality is surprisingly coherent for its size.”
“450M parameters with 8-language support and benchmark-leading vision grounding sounds great until you try to fine-tune it for a domain-specific task. The LEAP platform is still invite-only and the open weights lack fine-tuning docs. Worth watching but not shipping to prod yet.”
“Benchmarks are one thing; real task performance is another. A 9x memory saving typically comes with a 15-30% quality drop on anything beyond simple Q&A. And 'scores 5 points higher than our previous 1-bit model' is a low bar when the previous model wasn't competitive with 4-bit quants.”
“The race to run capable VLMs on-device is the precursor to AI-native hardware. Liquid's non-Transformer architecture is showing that efficiency gains don't require the same trade-offs as quantization. This is what AI hardware of 2028 will be built around.”
“Browser-native LLMs with no server change the entire privacy calculus. If this scales to 13B+ parameter territory at comparable compression ratios, every personal AI assistant can run offline on consumer hardware. That's a trajectory worth tracking closely.”
“On-device vision that can call functions means camera-native apps that don't phone home. Think real-time style transfer, offline image tagging, or AR creative tools that actually work on a plane. The creator tooling implications are underrated.”
“WebGPU inference means I can build offline creative tools — grammar checkers, caption writers, image prompt expanders — without an API key or monthly cost. The 1.7B model is small enough to embed in a browser extension with manageable download size.”
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